Over the past few years, wavelets have become extremely popular in signal and imageprocessingapplications. The classical linear wavelet transform, however, performs a homogeneous smoothing of the signal contents whi...
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Over the past few years, wavelets have become extremely popular in signal and imageprocessingapplications. The classical linear wavelet transform, however, performs a homogeneous smoothing of the signal contents which, ill some cases, is not desirable. This has led to a growing interest in (nonlinear) wavelet representations that can preserve discontinuities, such as transitions and edges. In this paper, we present the construction of adaptive wavelets by means of all extension of the lifting scheme. The basic idea is to choose the update filters according to some decision criterion which depends on the local characteristics of the input signal. We show that these adaptive schemes yield lower entropies than schemes with fixed update filters, a property that is highly relevant in the context of compression. Moreover, we analyze the effect of a scalar uniform quantization and the stability in such adaptive wavelet decompositions. (c) 2005 Elsevier B.v. All rights reserved.
In this paper we show that if wavelet domain processing is used with digital restoration, then pixel-scale features can be restored exactly in the absence of noise. In the presence of noise results are similar, except...
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ISBN:
(纸本)0819425915
In this paper we show that if wavelet domain processing is used with digital restoration, then pixel-scale features can be restored exactly in the absence of noise. In the presence of noise results are similar, except for some noise-amplification and ringing artifacts. wavelet domain modeling eliminates the need to discretize the image acquisition kernel and helps formulate image restoration as a discrete least squares problem. The performance of this technique is analyzed by model-based simulation using a comprehensive model to account for system blur at the image formation level, for the potentially important effects of aliasing, and for additive noise.
In this paper, a robust image hashing framework is proposed using image normalization, discrete wavelet transform and singular value decomposition. The stressed motive of the proposed scheme is to obtain a randomize h...
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ISBN:
(纸本)9781509055593
In this paper, a robust image hashing framework is proposed using image normalization, discrete wavelet transform and singular value decomposition. The stressed motive of the proposed scheme is to obtain a randomize hash sequence which can be used for image authentication and database search. For this purpose, the image is first normalized followed by hash generation in the wavelet domain utilizing the properties of singular value decomposition (SvD). Experimental evaluations demonstrate that the proposed scheme is providing the better robustness and security.
If G is an orthonormal system in IL2 then for any function g is an element of G the function g(2) is a probability density. In this paper we discuss the properties of wavelet based densities and corresponding random v...
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ISBN:
(纸本)0819425915
If G is an orthonormal system in IL2 then for any function g is an element of G the function g(2) is a probability density. In this paper we discuss the properties of wavelet based densities and corresponding random variables.
This paper investigates the relationship between the traditional wavelet (or matched filter) detector and the estimator correlator (EC) detector formulated in the wavelet domain. The EC detector is actually a weighted...
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ISBN:
(纸本)0819425915
This paper investigates the relationship between the traditional wavelet (or matched filter) detector and the estimator correlator (EC) detector formulated in the wavelet domain. The EC detector is actually a weighted wavelet detector, weighted by the scattering function that describes the medium and/or model. The wavelet detector is the optimum detector for point objects but it does not incorporate knowledge of the scattering environment. However, when imaging distributed objects, it is advantageous to take a priori information into account. The EC incorporates this information as a weight on the waveletimage and formulates an estimated spreading function which essentially achieves recombination of highlights and multipath energy. It can be shown that the EC reduces to the the wavelet detector when a point object is being imaged.
We give many examples of bivariate nonseparable compactly supported orthonormal wavelets which are supported over [0,3]x[0,3]. The Holder continuity properties of these wavelets are studied.
ISBN:
(纸本)0819425915
We give many examples of bivariate nonseparable compactly supported orthonormal wavelets which are supported over [0,3]x[0,3]. The Holder continuity properties of these wavelets are studied.
wavelet transforms have proven to be useful tools for several applications, including signal analysis, signal compression and numerical analysis. This paper surveys the vLSI architectures that have been proposed for c...
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wavelet transforms have proven to be useful tools for several applications, including signal analysis, signal compression and numerical analysis. This paper surveys the vLSI architectures that have been proposed for computing the Discrete and Continuous wavelet Transforms for I-D and 2-D signals. The architectures are based upon on-line versions of the wavelet transform algorithms. These architectures support single chip implementations and are optimal with respect to both area and time under the word-serial model.
We present a viewpoint of studying biorthogonal wavelets by using wavelet operators. A characterization of MRA biorthogonal wavelets is given in the framework of wavelet operators. An efficient wavelet filtering algor...
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ISBN:
(纸本)0819425915
We present a viewpoint of studying biorthogonal wavelets by using wavelet operators. A characterization of MRA biorthogonal wavelets is given in the framework of wavelet operators. An efficient wavelet filtering algorithm based on this characterization is applied to X-ray computerized tomography (CT) for multiresolution reconstruction and reduced X-ray exposure. Simulation results indicate that wavelet based reconstruction allows satisfactory image quality in a region of interest from local wavelet and global scaling components of projection data. The results are directly applicable to medical X-ray CT.
wavelet transform coding image compression is applied to two raw seismic data sets. The parameters of filter length, depth of decomposition, and quantization method are varied through 36 parameter settings and the rat...
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ISBN:
(纸本)0819425915
wavelet transform coding image compression is applied to two raw seismic data sets. The parameters of filter length, depth of decomposition, and quantization method are varied through 36 parameter settings and the rate-distortion relation is plotted and fitted with a line. The lines are compared to judge which parameter setting produces the highest quality for a given compression ratio on the sample data. It is found that long filters, moderate decomposition depths, and frequency-weighted, variance-adjusted quantization yield the best results.
The problem of image feature extraction for classification is difficult because of the high dimensionality inherent in image data. By extracting only relevant image features we reduce the dimensionality of the problem...
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ISBN:
(纸本)0819425915
The problem of image feature extraction for classification is difficult because of the high dimensionality inherent in image data. By extracting only relevant image features we reduce the dimensionality of the problem and improve classification accuracy. We further enhance classification performance by finding an optimal representation of the extracted image features which maximizes separability distance among classes. The principal tools used are Fourier series, wavelet packets, local discriminant basis analysis, and neural networks.
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